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Hourly PV production estimation by means of an exportable multiple linear regression model.

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Title: Hourly PV production estimation by means of an exportable multiple linear regression model.
Authors: Trigo-González, Mauricio1, Batlles, F.J.2,3, Alonso-Montesinos, Joaquín1,2,3 joaquin.alonso@ual.es, Ferrada, Pablo1, del Sagrado, J.4, Martínez-Durbán, M.4, Cortés, Marcelo1, Portillo, Carlos1, Marzo, Aitor1
Source: Renewable Energy: An International Journal. May2019, Vol. 135, p303-312. 10p.
Subject Terms: *Photovoltaic power systems, *Photovoltaic power generation, *Climatology, Cadmium telluride, Silicon
Abstract: Abstract The current state of photovoltaic (PV) electricity integration is demanding several strategies that control the optimal performance of PV plants. Cleaning the PV plant, controlling PV production or the estimation of the electricity generation, are some relevant items related to the PV systems. In general, the soiling, the clouds and another climatological factorsare involved in the final PV production. For knowing the performance of a PV system, it is necessity to model the PV plant behavior according to these relevant variables. In this work, a Multiple Linear Regression (MLR) model has been presented to determine the hourly PV production by using the Performance Ratio (PR) factor, according to different technologies: Cadmium Telluride (CdTe) and multicrystallinesilicon (mc-Si). In this sense, data from several PV plants were studied in different Chile regions: San Pedro de Atacama and Antofagasta. With this study, it has been determined that the model can be extrapolated to different climatological emplacements, where generally, the root mean square error (RMSE) presents values lower than 16% in all cases, having the best result the CdTetechnology. Highlights • The hourly PV production was estimated using Linear Regression for Chilean places. • Performance Ratio was aggregated in the model for considering soiling losses. • The model was extrapolated from desert to coast being better for CdTetechnologies. • In general, the PV estimation model presented RMSEvalues near to 8% for PV plants. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
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Abstract:Abstract The current state of photovoltaic (PV) electricity integration is demanding several strategies that control the optimal performance of PV plants. Cleaning the PV plant, controlling PV production or the estimation of the electricity generation, are some relevant items related to the PV systems. In general, the soiling, the clouds and another climatological factorsare involved in the final PV production. For knowing the performance of a PV system, it is necessity to model the PV plant behavior according to these relevant variables. In this work, a Multiple Linear Regression (MLR) model has been presented to determine the hourly PV production by using the Performance Ratio (PR) factor, according to different technologies: Cadmium Telluride (CdTe) and multicrystallinesilicon (mc-Si). In this sense, data from several PV plants were studied in different Chile regions: San Pedro de Atacama and Antofagasta. With this study, it has been determined that the model can be extrapolated to different climatological emplacements, where generally, the root mean square error (RMSE) presents values lower than 16% in all cases, having the best result the CdTetechnology. Highlights • The hourly PV production was estimated using Linear Regression for Chilean places. • Performance Ratio was aggregated in the model for considering soiling losses. • The model was extrapolated from desert to coast being better for CdTetechnologies. • In general, the PV estimation model presented RMSEvalues near to 8% for PV plants. [ABSTRACT FROM AUTHOR]
ISSN:09601481
DOI:10.1016/j.renene.2018.12.014